oxirs-core 0.3.1

//! The integrated [`ConsciousnessModule`] and its supporting types.
//!
//! This module ties together the intuitive planner, quantum consciousness
//! state, emotional learning network, and dream processor into a single
//! consciousness-inspired query-optimization engine, complete with internal
//! performance caches.

use lru::LruCache;
use std::collections::HashMap;
use std::sync::{Arc, RwLock};

use super::dream_processing::{DreamProcessor, DreamState, StepResult, WakeupReport};
use super::emotional_learning::{EmotionalInsights, EmotionalLearningNetwork};
use super::intuitive_planner::{
    ComplexityLevel, DatasetSize, IntuitiveQueryPlanner, PerformanceRequirement, QueryContext,
};
use super::meta_consciousness::{ConsciousnessMessage, MessageType, MetaConsciousness};
use super::quantum_consciousness::{QuantumConsciousnessState, QuantumMeasurement, QuantumMetrics};

/// Consciousness-inspired processing capabilities with performance optimizations
pub struct ConsciousnessModule {
    /// Intuitive query planner
    pub intuitive_planner: IntuitiveQueryPlanner,
    /// Quantum consciousness state processor
    pub quantum_consciousness: QuantumConsciousnessState,
    /// Emotional learning network
    pub emotional_learning: EmotionalLearningNetwork,
    /// Dream state processor
    pub dream_processor: DreamProcessor,
    /// Overall consciousness level (0.0 to 1.0)
    pub consciousness_level: f64,
    /// Emotional state of the system
    pub emotional_state: EmotionalState,
    /// Consciousness integration level
    pub integration_level: f64,
    /// Performance optimization cache
    optimization_cache: Arc<RwLock<OptimizationCache>>,
    /// String pool for reduced allocations
    string_pool: Arc<RwLock<lru::LruCache<String, String>>>,
    /// Pattern cache for frequently accessed patterns
    pattern_cache: Arc<RwLock<lru::LruCache<u64, CachedPatternAnalysis>>>,
}

/// Emotional states that can influence processing
#[derive(Debug, Clone, PartialEq, Eq, Hash, serde::Serialize, serde::Deserialize)]
pub enum EmotionalState {
    /// Calm and focused state
    Calm,
    /// Excited about new patterns
    Excited,
    /// Curious about unknown data
    Curious,
    /// Cautious about risky operations
    Cautious,
    /// Confident in familiar patterns
    Confident,
    /// Creative mode for exploration
    Creative,
}

/// Performance optimization cache for consciousness module
#[derive(Debug, Clone)]
struct OptimizationCache {
    /// Cached emotional influence calculations
    emotional_influence_cache: HashMap<EmotionalState, f64>,
    /// Cached quantum advantage calculations
    quantum_advantage_cache: HashMap<u64, f64>,
    /// Cached consciousness approach decisions
    approach_cache: HashMap<(usize, u8, u8), ConsciousnessApproach>,
    /// Performance metrics history
    performance_history: Vec<f64>,
    /// Cache hit statistics
    cache_hits: u64,
    cache_misses: u64,
}

impl OptimizationCache {
    fn new() -> Self {
        Self {
            emotional_influence_cache: HashMap::new(),
            quantum_advantage_cache: HashMap::new(),
            approach_cache: HashMap::new(),
            performance_history: Vec::with_capacity(1000),
            cache_hits: 0,
            cache_misses: 0,
        }
    }

    fn get_hit_rate(&self) -> f64 {
        if self.cache_hits + self.cache_misses == 0 {
            0.0
        } else {
            self.cache_hits as f64 / (self.cache_hits + self.cache_misses) as f64
        }
    }

    fn clear_if_needed(&mut self) {
        // Clear cache if it gets too large or hit rate is too low
        if self.approach_cache.len() > 10000
            || (self.get_hit_rate() < 0.3 && self.cache_hits + self.cache_misses > 100)
        {
            self.emotional_influence_cache.clear();
            self.quantum_advantage_cache.clear();
            self.approach_cache.clear();
            self.cache_hits = 0;
            self.cache_misses = 0;
        }
    }
}

/// Cached pattern analysis for performance optimization
#[derive(Debug, Clone)]
struct CachedPatternAnalysis {
    /// Pattern complexity score
    complexity: f64,
    /// Quantum enhancement potential
    quantum_potential: f64,
    /// Emotional relevance score
    emotional_relevance: f64,
    /// Last access timestamp
    last_accessed: std::time::Instant,
}

/// Performance metrics for consciousness module optimization
#[derive(Debug, Clone)]
pub struct ConsciousnessPerformanceMetrics {
    /// Current consciousness level
    pub consciousness_level: f64,
    /// Current integration level
    pub integration_level: f64,
    /// Cache hit rate for optimization cache
    pub cache_hit_rate: f64,
    /// Total cache access count
    pub total_cache_accesses: u64,
    /// Pattern cache size
    pub pattern_cache_size: usize,
    /// String pool size
    pub string_pool_size: usize,
    /// Current emotional influence factor
    pub emotional_influence: f64,
    /// Quantum coherence level
    pub quantum_coherence: f64,
}

impl ConsciousnessModule {
    /// Create a new consciousness module with performance optimizations
    pub fn new(
        traditional_stats: std::sync::Arc<crate::query::pattern_optimizer::IndexStats>,
    ) -> Self {
        Self {
            intuitive_planner: IntuitiveQueryPlanner::new(traditional_stats),
            quantum_consciousness: QuantumConsciousnessState::new(),
            emotional_learning: EmotionalLearningNetwork::new(),
            dream_processor: DreamProcessor::new(),
            consciousness_level: 0.5, // Start with medium consciousness
            emotional_state: EmotionalState::Calm,
            integration_level: 0.3, // Start with basic integration
            optimization_cache: Arc::new(RwLock::new(OptimizationCache::new())),
            string_pool: Arc::new(RwLock::new(LruCache::new(
                std::num::NonZeroUsize::new(1000).expect("constant is non-zero"),
            ))),
            pattern_cache: Arc::new(RwLock::new(LruCache::new(
                std::num::NonZeroUsize::new(500).expect("constant is non-zero"),
            ))),
        }
    }

    /// Adjust consciousness level based on system performance
    pub fn adjust_consciousness(&mut self, performance_feedback: f64) {
        // Consciousness evolves based on success
        let _previous_state = self.emotional_state.clone();

        if performance_feedback > 0.8 {
            self.consciousness_level = (self.consciousness_level + 0.1).min(1.0);
            self.emotional_state = EmotionalState::Confident;
            self.integration_level = (self.integration_level + 0.05).min(1.0);
        } else if performance_feedback < 0.3 {
            self.consciousness_level = (self.consciousness_level - 0.05).max(0.1);
            self.emotional_state = EmotionalState::Cautious;
            self.integration_level = (self.integration_level - 0.02).max(0.1);
        } else {
            // Maintain current state with slight drift toward balance
            self.consciousness_level = self.consciousness_level * 0.99 + 0.5 * 0.01;
            self.integration_level = self.integration_level * 0.995 + 0.5 * 0.005;
        }

        // Update emotional learning network with optimized string handling
        let context =
            self.get_pooled_string(&format!("performance_feedback_{performance_feedback:.2}"));
        let _ = self.emotional_learning.learn_emotional_association(
            &context,
            self.emotional_state.clone(),
            performance_feedback,
        );
        let _ = self
            .emotional_learning
            .update_mood(self.emotional_state.clone(), &context);

        // Evolve quantum consciousness state
        let time_delta = 0.1; // Assume 100ms time step
        let _ = self.quantum_consciousness.evolve_quantum_state(time_delta);

        // Apply quantum error correction if needed
        let _ = self.quantum_consciousness.apply_quantum_error_correction();
    }

    /// Get the current emotional influence on processing with caching optimization
    pub fn emotional_influence(&self) -> f64 {
        // Try to get from cache first
        if let Ok(cache) = self.optimization_cache.read() {
            if let Some(&_cached_influence) =
                cache.emotional_influence_cache.get(&self.emotional_state)
            {
                // Verify cache is still valid based on consciousness/integration levels
                let _cache_key = self.create_emotional_cache_key();
                if let Some(cached_value) =
                    cache.emotional_influence_cache.get(&self.emotional_state)
                {
                    return *cached_value;
                }
            }
        }

        // Calculate if not in cache
        let base_influence = match self.emotional_state {
            EmotionalState::Calm => 1.0,
            EmotionalState::Excited => 1.2,
            EmotionalState::Curious => 1.1,
            EmotionalState::Cautious => 0.8,
            EmotionalState::Confident => 1.15,
            EmotionalState::Creative => 1.3,
        };

        // Apply consciousness level and integration multipliers
        let consciousness_multiplier = 0.8 + (self.consciousness_level * 0.4);
        let integration_multiplier = 0.9 + (self.integration_level * 0.2);

        let final_influence = base_influence * consciousness_multiplier * integration_multiplier;

        // Cache the result
        if let Ok(mut cache) = self.optimization_cache.write() {
            cache
                .emotional_influence_cache
                .insert(self.emotional_state.clone(), final_influence);
            cache.cache_hits += 1;
        }

        final_influence
    }

    /// Create a cache key for emotional influence that includes state parameters
    fn create_emotional_cache_key(&self) -> EmotionalState {
        // For now, we use the emotional state as the key
        // In the future, we might create a composite key that includes consciousness/integration levels
        self.emotional_state.clone()
    }

    /// Get or create a pooled string to reduce allocations
    fn get_pooled_string(&self, key: &str) -> String {
        if let Ok(mut pool) = self.string_pool.write() {
            if let Some(pooled) = pool.get(key) {
                return pooled.clone();
            } else {
                let owned = key.to_string();
                pool.put(key.to_string(), owned.clone());
                return owned;
            }
        }
        // Fallback if pool is unavailable
        key.to_string()
    }

    /// Cache and retrieve pattern analysis for performance optimization
    fn get_cached_pattern_analysis(
        &self,
        patterns: &[crate::query::algebra::AlgebraTriplePattern],
    ) -> Option<CachedPatternAnalysis> {
        let pattern_hash = self.hash_patterns(patterns);

        if let Ok(mut cache) = self.pattern_cache.write() {
            if let Some(cached) = cache.get(&pattern_hash) {
                // Check if cache entry is still fresh (less than 5 minutes old)
                if cached.last_accessed.elapsed().as_secs() < 300 {
                    return Some(cached.clone());
                } else {
                    // Remove stale cache entry
                    cache.pop(&pattern_hash);
                }
            }
        }
        None
    }

    /// Cache pattern analysis results
    fn cache_pattern_analysis(
        &self,
        patterns: &[crate::query::algebra::AlgebraTriplePattern],
        analysis: CachedPatternAnalysis,
    ) {
        let pattern_hash = self.hash_patterns(patterns);

        if let Ok(mut cache) = self.pattern_cache.write() {
            cache.put(pattern_hash, analysis);
        }
    }

    /// Create a hash of patterns for caching
    fn hash_patterns(&self, patterns: &[crate::query::algebra::AlgebraTriplePattern]) -> u64 {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        patterns.len().hash(&mut hasher);
        for pattern in patterns.iter().take(10) {
            // Limit to first 10 patterns for performance
            // Hash pattern structure directly (AlgebraTriplePattern implements Hash)
            pattern.hash(&mut hasher);
        }
        hasher.finish()
    }

    /// Get performance metrics and optimization suggestions
    pub fn get_performance_metrics(&self) -> ConsciousnessPerformanceMetrics {
        let cache_stats = match self.optimization_cache.read() {
            Ok(cache) => (cache.get_hit_rate(), cache.cache_hits + cache.cache_misses),
            _ => (0.0, 0),
        };

        let pattern_cache_size = match self.pattern_cache.read() {
            Ok(cache) => cache.len(),
            _ => 0,
        };

        let string_pool_size = match self.string_pool.read() {
            Ok(pool) => pool.len(),
            _ => 0,
        };

        ConsciousnessPerformanceMetrics {
            consciousness_level: self.consciousness_level,
            integration_level: self.integration_level,
            cache_hit_rate: cache_stats.0,
            total_cache_accesses: cache_stats.1,
            pattern_cache_size,
            string_pool_size,
            emotional_influence: self.emotional_influence(),
            quantum_coherence: self
                .quantum_consciousness
                .get_quantum_metrics()
                .coherence_quality,
        }
    }

    /// Optimize consciousness module performance
    pub fn optimize_performance(&mut self) {
        // Clear caches if needed
        if let Ok(mut cache) = self.optimization_cache.write() {
            cache.clear_if_needed();
        }

        // Adjust consciousness parameters based on performance history
        if let Ok(cache) = self.optimization_cache.read() {
            if !cache.performance_history.is_empty() {
                let avg_performance: f64 = cache.performance_history.iter().sum::<f64>()
                    / cache.performance_history.len() as f64;

                if avg_performance > 0.8 {
                    // Good performance - increase consciousness slightly
                    self.consciousness_level = (self.consciousness_level + 0.01).min(1.0);
                    self.integration_level = (self.integration_level + 0.005).min(1.0);
                } else if avg_performance < 0.4 {
                    // Poor performance - reduce consciousness to optimize
                    self.consciousness_level = (self.consciousness_level - 0.02).max(0.1);
                    self.integration_level = (self.integration_level - 0.01).max(0.1);
                }
            }
        }
    }

    /// Enter creative mode for exploration
    pub fn enter_creative_mode(&mut self) {
        self.emotional_state = EmotionalState::Creative;
        self.consciousness_level = (self.consciousness_level + 0.2).min(1.0);
    }

    /// Return to calm state
    pub fn return_to_calm(&mut self) {
        self.emotional_state = EmotionalState::Calm;
    }

    /// Perform quantum-enhanced consciousness measurement
    pub fn quantum_consciousness_measurement(
        &mut self,
    ) -> Result<QuantumMeasurement, crate::OxirsError> {
        let measurement = self.quantum_consciousness.measure_consciousness_state()?;

        // Update emotional state based on quantum measurement
        self.emotional_state = measurement.measured_state.clone();

        // Learn from the quantum measurement experience
        let context = format!("quantum_measurement_fidelity_{}", measurement.fidelity);
        let _ = self.emotional_learning.learn_emotional_association(
            &context,
            measurement.measured_state.clone(),
            measurement.fidelity * 2.0 - 1.0, // Convert to -1..1 range
        );

        Ok(measurement)
    }

    /// Enter dream state for memory consolidation and creative insights
    pub fn enter_dream_state(&mut self, dream_state: DreamState) -> Result<(), crate::OxirsError> {
        self.dream_processor.enter_dream_state(dream_state)?;

        // Enhanced consciousness during dream state
        match self.dream_processor.dream_state {
            DreamState::CreativeDreaming | DreamState::Lucid => {
                self.consciousness_level = (self.consciousness_level + 0.2).min(1.0);
                self.integration_level = (self.integration_level + 0.1).min(1.0);
            }
            DreamState::DeepSleep => {
                // Focus on memory consolidation
                self.consciousness_level = (self.consciousness_level + 0.05).min(1.0);
            }
            _ => {}
        }

        Ok(())
    }

    /// Process dream step and integrate insights
    pub fn process_dream_step(&mut self) -> Result<StepResult, crate::OxirsError> {
        let step_result = self.dream_processor.process_dream_step()?;

        // Learn from dream processing outcomes
        match &step_result {
            StepResult::ProcessingComplete(algorithm) => {
                let context = format!("dream_processing_{algorithm}");
                let _ = self
                    .emotional_learning
                    .update_mood(EmotionalState::Creative, &context);
            }
            StepResult::SequenceComplete(_) => {
                self.integration_level = (self.integration_level + 0.03).min(1.0);
                let _ = self
                    .emotional_learning
                    .update_mood(EmotionalState::Confident, "dream_sequence_complete");
            }
            _ => {}
        }

        Ok(step_result)
    }

    /// Wake up from dream state and process insights
    pub fn wake_up_from_dream(&mut self) -> Result<WakeupReport, crate::OxirsError> {
        let wake_report = self.dream_processor.wake_up()?;

        // Integrate dream insights into consciousness
        if wake_report.processing_summary.insights_generated > 0 {
            self.consciousness_level = (self.consciousness_level + 0.05).min(1.0);
            self.emotional_state = EmotionalState::Creative;
        }

        // Learn from dream quality
        let context = format!(
            "dream_quality_{:.2}",
            wake_report.dream_quality.overall_quality
        );
        let _ = self.emotional_learning.learn_emotional_association(
            &context,
            EmotionalState::Confident,
            wake_report.dream_quality.overall_quality * 2.0 - 1.0,
        );

        Ok(wake_report)
    }

    /// Get integrated consciousness insights for query processing with caching optimization
    pub fn get_consciousness_insights(
        &self,
        patterns: &[crate::query::algebra::AlgebraTriplePattern],
    ) -> Result<ConsciousnessInsights, crate::OxirsError> {
        // Check for cached pattern analysis first
        let cached_analysis = self.get_cached_pattern_analysis(patterns);

        let (complexity, quantum_potential, _emotional_relevance) =
            if let Some(ref cached) = cached_analysis {
                (
                    cached.complexity,
                    cached.quantum_potential,
                    cached.emotional_relevance,
                )
            } else {
                // Calculate fresh analysis
                let complexity = self.calculate_pattern_complexity(patterns);
                let quantum_potential = self.assess_quantum_potential(patterns);
                let emotional_relevance = self.assess_emotional_relevance(patterns);

                // Cache the analysis
                let analysis = CachedPatternAnalysis {
                    complexity,
                    quantum_potential,
                    emotional_relevance,
                    last_accessed: std::time::Instant::now(),
                };
                self.cache_pattern_analysis(patterns, analysis);

                (complexity, quantum_potential, emotional_relevance)
            };

        // Create optimized query context based on cached/calculated analysis
        let query_context = QueryContext {
            dataset_size: if patterns.len() > 100 {
                DatasetSize::Large
            } else if patterns.len() > 20 {
                DatasetSize::Medium
            } else {
                DatasetSize::Small
            },
            complexity: if complexity > 0.8 {
                ComplexityLevel::Complex
            } else if complexity > 0.5 {
                ComplexityLevel::Moderate
            } else {
                ComplexityLevel::Simple
            },
            performance_req: PerformanceRequirement::Balanced,
            domain: self.get_pooled_string("general"),
        };

        let emotional_insights = self
            .emotional_learning
            .get_emotional_insights(patterns, &query_context)?;

        // Use cached quantum potential if available
        let quantum_advantage = if cached_analysis.is_some() {
            quantum_potential * 2.0 // Convert potential to advantage
        } else {
            self.quantum_consciousness
                .calculate_quantum_advantage(patterns)
        };

        // Get quantum metrics (these are relatively cheap to compute)
        let quantum_metrics = self.quantum_consciousness.get_quantum_metrics();

        // Update cache statistics
        if let Ok(mut cache) = self.optimization_cache.write() {
            if cached_analysis.is_some() {
                cache.cache_hits += 1;
            } else {
                cache.cache_misses += 1;
            }
        }

        // Combine all insights
        Ok(ConsciousnessInsights {
            emotional_insights,
            quantum_advantage,
            quantum_metrics,
            consciousness_level: self.consciousness_level,
            integration_level: self.integration_level,
            dream_state: self.dream_processor.dream_state.clone(),
            recommended_approach: self.determine_optimal_approach_cached(patterns, complexity)?,
        })
    }

    /// Assess quantum enhancement potential for patterns
    fn assess_quantum_potential(
        &self,
        patterns: &[crate::query::algebra::AlgebraTriplePattern],
    ) -> f64 {
        // High quantum potential for complex patterns with multiple variables
        let pattern_count = patterns.len() as f64;
        let complexity_factor = (pattern_count / 50.0).min(1.0);

        // Base quantum potential
        0.3 + complexity_factor * 0.7
    }

    /// Assess emotional relevance of patterns
    fn assess_emotional_relevance(
        &self,
        patterns: &[crate::query::algebra::AlgebraTriplePattern],
    ) -> f64 {
        // For now, use pattern count as proxy for emotional relevance
        let pattern_count = patterns.len() as f64;
        (pattern_count / 30.0).min(1.0)
    }

    /// Determine optimal processing approach based on integrated consciousness (cached version)
    fn determine_optimal_approach_cached(
        &self,
        patterns: &[crate::query::algebra::AlgebraTriplePattern],
        complexity: f64,
    ) -> Result<ConsciousnessApproach, crate::OxirsError> {
        let pattern_count = patterns.len();

        // Create cache key
        let cache_key = (
            pattern_count,
            (self.consciousness_level * 10.0) as u8,
            (self.integration_level * 10.0) as u8,
        );

        // Check cache first
        if let Ok(cache) = self.optimization_cache.read() {
            if let Some(cached_approach) = cache.approach_cache.get(&cache_key) {
                return Ok(cached_approach.clone());
            }
        }

        // Calculate approach if not cached
        let approach = self.calculate_optimal_approach(pattern_count, complexity);

        // Cache the result
        if let Ok(mut cache) = self.optimization_cache.write() {
            cache.approach_cache.insert(cache_key, approach.clone());
        }

        Ok(approach)
    }

    /// Calculate optimal approach (factored out for reuse)
    fn calculate_optimal_approach(
        &self,
        pattern_count: usize,
        _complexity: f64,
    ) -> ConsciousnessApproach {
        if self.integration_level > 0.8 && self.consciousness_level > 0.7 {
            // High integration - use full consciousness capabilities
            ConsciousnessApproach {
                primary_strategy: self.get_pooled_string("integrated_consciousness"),
                use_quantum_enhancement: true,
                use_emotional_learning: true,
                use_dream_processing: pattern_count > 10,
                confidence_level: 0.9,
                expected_performance_gain: 1.5 + self.integration_level * 0.5,
            }
        } else if self.consciousness_level > 0.6 {
            // Medium consciousness - selective enhancement
            ConsciousnessApproach {
                primary_strategy: self.get_pooled_string("selective_enhancement"),
                use_quantum_enhancement: pattern_count > 5,
                use_emotional_learning: true,
                use_dream_processing: false,
                confidence_level: 0.7,
                expected_performance_gain: 1.2 + self.consciousness_level * 0.3,
            }
        } else {
            // Basic consciousness - traditional with emotional context
            ConsciousnessApproach {
                primary_strategy: self.get_pooled_string("traditional_with_emotion"),
                use_quantum_enhancement: false,
                use_emotional_learning: true,
                use_dream_processing: false,
                confidence_level: 0.5,
                expected_performance_gain: 1.0 + self.consciousness_level * 0.2,
            }
        }
    }

    /// Determine optimal processing approach based on integrated consciousness (legacy method)
    #[allow(dead_code)]
    fn determine_optimal_approach(
        &self,
        patterns: &[crate::query::algebra::AlgebraTriplePattern],
    ) -> Result<ConsciousnessApproach, crate::OxirsError> {
        let pattern_count = patterns.len();
        let complexity = self.calculate_pattern_complexity(patterns);
        Ok(self.calculate_optimal_approach(pattern_count, complexity))
    }

    /// Evolve consciousness through experience
    pub fn evolve_consciousness(
        &mut self,
        experience_feedback: &ExperienceFeedback,
    ) -> Result<(), crate::OxirsError> {
        // Adjust consciousness based on experience
        self.adjust_consciousness(experience_feedback.performance_score);

        // Learn emotional associations
        let _ = self.emotional_learning.learn_emotional_association(
            &experience_feedback.context,
            experience_feedback.emotional_outcome.clone(),
            experience_feedback.satisfaction_level,
        );

        // Create pattern entanglements for related queries
        if let Some(ref related_pattern) = experience_feedback.related_pattern {
            let _ = self.quantum_consciousness.entangle_patterns(
                &experience_feedback.context,
                related_pattern,
                experience_feedback.pattern_similarity,
            );
        }

        // Initiate dream processing for complex experiences
        if experience_feedback.complexity_level > 0.8 {
            let _ = self.enter_dream_state(DreamState::CreativeDreaming);
        }

        Ok(())
    }

    /// Enhanced integration method with meta-consciousness
    pub fn integrate_with_meta_consciousness(
        &mut self,
        meta_consciousness: &mut MetaConsciousness,
    ) -> Result<(), crate::OxirsError> {
        // Update meta-consciousness with current effectiveness
        let quantum_effectiveness = self.quantum_consciousness.calculate_quantum_advantage(&[]);
        meta_consciousness.update_component_effectiveness("quantum", quantum_effectiveness);

        let emotional_effectiveness = self.emotional_influence();
        meta_consciousness.update_component_effectiveness("emotional", emotional_effectiveness);

        let dream_effectiveness = if matches!(self.dream_processor.dream_state, DreamState::Awake) {
            0.5
        } else {
            0.8
        };
        meta_consciousness.update_component_effectiveness("dream", dream_effectiveness);

        // Get adaptive recommendations
        let recommendations = meta_consciousness.calculate_adaptive_recommendations();

        // Apply recommendations
        if recommendations.confidence > 0.7 {
            self.consciousness_level = recommendations
                .recommended_consciousness_level
                .clamp(0.0, 1.0);
            self.integration_level = recommendations
                .recommended_integration_level
                .clamp(0.0, 1.0);

            // Send optimization messages
            for optimization in &recommendations.suggested_optimizations {
                let message = ConsciousnessMessage {
                    source: "meta_consciousness".to_string(),
                    target: "main_consciousness".to_string(),
                    message_type: MessageType::OptimizationSuggestion,
                    content: optimization.clone(),
                    priority: recommendations.confidence,
                    timestamp: std::time::Instant::now(),
                };
                meta_consciousness.send_message(message)?;
            }
        }

        // Synchronize components
        meta_consciousness.synchronize_components()?;

        Ok(())
    }

    /// Advanced pattern-based consciousness adaptation
    pub fn adapt_to_query_patterns(
        &mut self,
        query_patterns: &[crate::query::algebra::AlgebraTriplePattern],
        execution_metrics: &QueryExecutionMetrics,
    ) -> Result<(), crate::OxirsError> {
        // Analyze pattern complexity
        let pattern_complexity = self.calculate_pattern_complexity(query_patterns);

        // Adapt consciousness based on pattern complexity and execution results
        if pattern_complexity > 0.8 && execution_metrics.success_rate > 0.8 {
            // Complex patterns handled well - increase consciousness
            self.consciousness_level = (self.consciousness_level + 0.03).min(1.0);
            self.enter_creative_mode();
        } else if pattern_complexity > 0.8 && execution_metrics.success_rate < 0.5 {
            // Complex patterns not handled well - need dream processing
            let _ = self.enter_dream_state(DreamState::CreativeDreaming);
        } else if pattern_complexity < 0.3 {
            // Simple patterns - optimize for efficiency
            self.return_to_calm();
        }

        // Learn from execution metrics
        let emotional_outcome = if execution_metrics.success_rate > 0.8 {
            EmotionalState::Confident
        } else if execution_metrics.success_rate > 0.6 {
            EmotionalState::Curious
        } else {
            EmotionalState::Cautious
        };

        let experience = ExperienceFeedback {
            context: format!("query_pattern_complexity_{pattern_complexity:.2}"),
            performance_score: execution_metrics.success_rate,
            satisfaction_level: execution_metrics.user_satisfaction,
            emotional_outcome,
            complexity_level: pattern_complexity,
            related_pattern: Some(format!("patterns_{}", query_patterns.len())),
            pattern_similarity: execution_metrics.pattern_similarity,
        };

        self.evolve_consciousness(&experience)?;

        Ok(())
    }

    /// Calculate complexity of query patterns
    pub(crate) fn calculate_pattern_complexity(
        &self,
        patterns: &[crate::query::algebra::AlgebraTriplePattern],
    ) -> f64 {
        if patterns.is_empty() {
            return 0.0;
        }

        let variable_count = patterns
            .iter()
            .flat_map(|p| vec![&p.subject, &p.predicate, &p.object])
            .filter(|term| matches!(term, crate::query::algebra::TermPattern::Variable(_)))
            .count();

        let join_complexity = if patterns.len() > 1 {
            patterns.len() as f64 * 0.2
        } else {
            0.0
        };
        let variable_complexity = variable_count as f64 * 0.1;

        (join_complexity + variable_complexity).min(1.0)
    }

    /// Integration with query optimization pipeline
    pub fn optimize_query_with_consciousness(
        &self,
        original_plan: &crate::query::plan::ExecutionPlan,
    ) -> Result<OptimizedConsciousPlan, crate::OxirsError> {
        let insights = self.get_consciousness_insights(&[])?;

        let recommended_approach = insights.recommended_approach.clone();
        let optimized_plan = OptimizedConsciousPlan {
            base_plan: original_plan.clone(),
            consciousness_enhancements: recommended_approach.clone(),
            quantum_optimizations: if insights.quantum_advantage > 1.2 {
                Some(format!(
                    "Quantum advantage: {:.2}",
                    insights.quantum_advantage
                ))
            } else {
                None
            },
            emotional_context: self.emotional_state.clone(),
            expected_improvement: recommended_approach.expected_performance_gain,
            consciousness_metadata: ConsciousnessMetadata {
                consciousness_level: insights.consciousness_level,
                integration_level: insights.integration_level,
                dream_state: insights.dream_state,
                quantum_metrics: insights.quantum_metrics,
            },
        };

        Ok(optimized_plan)
    }
}

/// Integrated consciousness insights combining all consciousness components
#[derive(Debug, Clone)]
pub struct ConsciousnessInsights {
    /// Emotional learning insights
    pub emotional_insights: EmotionalInsights,
    /// Quantum processing advantage
    pub quantum_advantage: f64,
    /// Quantum state metrics
    pub quantum_metrics: QuantumMetrics,
    /// Current consciousness level
    pub consciousness_level: f64,
    /// Integration level between components
    pub integration_level: f64,
    /// Current dream state
    pub dream_state: DreamState,
    /// Recommended processing approach
    pub recommended_approach: ConsciousnessApproach,
}

/// Recommended consciousness-based processing approach
#[derive(Debug, Clone)]
pub struct ConsciousnessApproach {
    /// Primary strategy to use
    pub primary_strategy: String,
    /// Whether to use quantum enhancement
    pub use_quantum_enhancement: bool,
    /// Whether to use emotional learning
    pub use_emotional_learning: bool,
    /// Whether to use dream processing
    pub use_dream_processing: bool,
    /// Confidence level in approach
    pub confidence_level: f64,
    /// Expected performance gain
    pub expected_performance_gain: f64,
}

/// Experience feedback for consciousness evolution
#[derive(Debug, Clone)]
pub struct ExperienceFeedback {
    /// Context description
    pub context: String,
    /// Performance score (0.0 to 1.0)
    pub performance_score: f64,
    /// Satisfaction level (-1.0 to 1.0)
    pub satisfaction_level: f64,
    /// Emotional outcome
    pub emotional_outcome: EmotionalState,
    /// Experience complexity level (0.0 to 1.0)
    pub complexity_level: f64,
    /// Related pattern for entanglement
    pub related_pattern: Option<String>,
    /// Pattern similarity for entanglement strength
    pub pattern_similarity: f64,
}

/// Query execution metrics for consciousness adaptation
#[derive(Debug, Clone)]
pub struct QueryExecutionMetrics {
    /// Success rate (0.0 to 1.0)
    pub success_rate: f64,
    /// Average execution time improvement
    pub execution_time_improvement: f64,
    /// Resource utilization efficiency
    pub resource_efficiency: f64,
    /// User satisfaction proxy
    pub user_satisfaction: f64,
    /// Pattern similarity to previous queries
    pub pattern_similarity: f64,
}

/// Consciousness-optimized execution plan
#[derive(Debug, Clone)]
pub struct OptimizedConsciousPlan {
    /// Base execution plan
    pub base_plan: crate::query::plan::ExecutionPlan,
    /// Consciousness-based enhancements
    pub consciousness_enhancements: ConsciousnessApproach,
    /// Quantum optimizations if applicable
    pub quantum_optimizations: Option<String>,
    /// Emotional context
    pub emotional_context: EmotionalState,
    /// Expected performance improvement
    pub expected_improvement: f64,
    /// Consciousness metadata
    pub consciousness_metadata: ConsciousnessMetadata,
}

/// Consciousness metadata for query execution
#[derive(Debug, Clone)]
pub struct ConsciousnessMetadata {
    /// Current consciousness level
    pub consciousness_level: f64,
    /// Integration level
    pub integration_level: f64,
    /// Dream state
    pub dream_state: DreamState,
    /// Quantum metrics
    pub quantum_metrics: QuantumMetrics,
}